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Assistant Professor

**Center for Computational and Data-Intensive Science and Engineering**

Vladimir graduated from Lomonosov Moscow State University in 2007, where he received his PhD degree in 2010 in the field of Statistical and Polymer Physics studying phase transition phenomena in copolymer and polyelectrolyte systems. Later he moved to work on optimisation problems in systems with anomalous diffusion at the Technical University of Munich and University of Potsdam in Germany. The mail focus of the work was target search optimisation, first-passage and first-hitting properties. Before moving to Skoltech Vladimir worked at the University of Cambridge, UK developing a framework for prediction of mechanical properties of amorphous polymers from their microscopic structure. Vladimir has authored about 20 papers in peer-reviewed journals including a few of highly cited review papers and papers in the top journals like PNAS. During his work in Munich and Cambridge he actively participated in teaching of statistical physics and maths courses.

As a faculty member, Prof. V.V. Palyulin provides teaching at graduate level, advising student and innovation projects, and conducting application-oriented research.

His current research interests include various fields of statistical physics and stochastic processes research such as trajectory analysis, reinforcement learning for target search, anomalous diffusion and crowding problems.

**Stochastic processes and phenomena****Target search optimisation****Machine learning applications in statistical physics****Reinforcement learning for search optimisation****Mathematical modelling of soft matter****Mathematical modelling of traffic problems**

**Selected publications**

1. Palyulin V.V., Chechkin A.V., Metzler R., “Levy flights do not always optimize random blind search for sparse targets”, Proc. Natl. Acad. Sci. USA, 2014, 111, 2931-2936

2. Palyulin V.V., Blackburn G., Lomholt M.A., Watkins N.W., Metzler R., Klages R., Chechkin A.V., “First-passage and first-hitting of Levy flights and Levy walks”, New J. Phys., 2019, 21, 103028.

3. Palyulin V.V., Ala-Nissila T., Metzler R., “Polymer translocation: the first two decades and the recent diversification”, Soft Matter, 2014, 10, 9016-9037

4. Palyulin V.V., Ness C., Milkus R., Elder R.M., Sirk T., and Zaccone A., “Parameter-free predictions of the viscoelastic response of glassy polymers from non-affine lattice dynamics”, Soft Matter, 2018, 14, 8475-8482

5. Palyulin V.V., Chechkin A.V., Klages R., Metzler R., “Search reliability and search efficiency of combined Levy-Brownian motion”, J. Phys. A, Math. Theor., 2016, 49, 394002

**List of papers**

Research scientist position available in Statistical Physics and Machine Learning Group — Skoltech

Skolkovo Institute of Science and Technology (Skoltech) is a unique English-speaking international research university, located just outside of Moscow. Established in collaboration with M.I.T., Skoltech integrates and modernises the best Russian scientific traditions.

A two-year research scientist position with a possibility of a one year extension is being offered to work in Skoltech’s Statistical Physics and Machine Learning Group — led by Prof. Vladimir Palyulin.

The main project will focus on the implementation of machine learning methods for study of active matter. This includes search optimisation in complex systems and on complex networks, collective motion of active particles and anomalous diffusion properties.

An ideal candidate will have a PhD with research experience in statistical physics and/or machine learning methods. The monthly salary ranges between 140k RUB (70 RUB = 1 Euro) and 180k RUB depending on the experience. Medical insurance and partial costs of accommodation as well as moving costs can also be covered. Desired start dates are between January and March 2020.Review of applications will continue until the position is filled. Inquiries about this position and applications should be directed to Prof. Vladimir Palyulin via V.Palyulin@skoltech.ru

Maria Larchenko

Ph.D. student

- Stochastic Methods in Mathematical Modelling, Term 4

Syllabus can be found here, http://files.skoltech.ru/data/edu/syllabuses/2019/MA03363.pdf